Two Means of Calculating Very Low Failure Probability
Bibliographic record
Abstract
Computation of large break probabilities in pipes when initiating cracks are the dominant degradation mode is difficult, because the problem is dominated by the probability of initiating multiple cracks around the pipe circumference and having them coalesce and grow to become long prior to penetrating the wall to become a leak. The purpose of this paper is to describe two techniques for evaluating very low large break probabilities in pipes with multiple initiating cracks: (i) combining initiation and growth probabilities by a convolution integral, and (ii) sorting through sets of sampled random variables and performing detailed (lifetime) calculations only for particularly “severe” sets. These techniques are demonstrated in an example problem involving primary water stress corrosion crack (PWSCC) initiation and subsequent growth in a piping weldment with high residual stresses by use of a probabilistic fracture mechanics code, PRAISE-CANDU, which is under development to address specific degradation issues in CANDU® reactors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".